tf.compat.v1.nn.moments
Calculate the mean and variance of x.
tf.compat.v1.nn.moments(
x, axes, shift=None, name=None, keep_dims=None, keepdims=None
)
The mean and variance are calculated by aggregating the contents of x across axes. If x is 1-D and axes = [0] this is just the mean and variance of a vector.
Note: shift is currently not used; the true mean is computed and used.
When using these moments for batch normalization (see tf.nn.batch_normalization):
- for so-called "global normalization", used with convolutional filters with shape
[batch, height, width, depth], passaxes=[0, 1, 2]. - for simple batch normalization pass
axes=[0](batch only).
| Args | |
|---|---|
x |
A Tensor. |
axes |
Array of ints. Axes along which to compute mean and variance. |
shift |
Not used in the current implementation |
name |
Name used to scope the operations that compute the moments. |
keep_dims |
produce moments with the same dimensionality as the input. |
keepdims |
Alias to keep_dims. |
| Returns | |
|---|---|
Two Tensor objects: mean and variance. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/nn/moments